SenseMyCity Overview

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Information about SenseMyCity Overview

Published on March 10, 2014

Author: AnaAguiar4



This is an overview of what our crowdsensing platform is and can achieve, including examples from a beta data collection and examples of single case and longitudinal stress study. The latter were carried out in cooperation with Prof. João Paulo Cunha, Prof. Mariana Kaiseler, Prof. Cristina Queirós.

      The  Future  Ci+es  Crowdsensor   João  Rodrigues,  Vítor  Ribeiro,  Ana  Aguiar,  João  Barros   FEUP,  IT  

What  is  a  crowdsensor?   •  Leveraging  the  power  of  the  crowd   •  Use  people  as  moving  sensors  to  gather  data     •  Smartphones  are  a  ubiquitous  plaIorm  with   many  connecKvity  possibiliKes   –  22%  of  the  world  populaKon  by  the  end  of  2013   •  Business  Insider  Intelligence,  Dec  2013   –  39.6%  of  mobile  phone  users  in  Portugal   •  Marktest,  Aug  2013   –  50%  of  the  15-­‐24  age  group   •  Marktest,  Aug  2013     –  These  figures  are  growing  

•  Modular  and  stable  mobile  sensing  plaIorm   –  ApplicaKon  for  Android  OS   –  Cloud  storage   –  VisualisaKon  plaIorm     CrowdSensor  

SenseMyCity  App   •  Internal  Sensors   –  LocaKon:  GPS,  Network   –  MoKon:  accelerometer,   gyroscope,  magnetometer   –  ConnecKvity:  WiFi,  cellular   –  Environmental:  temperature,   pressure,  humidity   •  External  Sensors   –  On-­‐Board  DiagnosKcs  (OBD)   –  Vital  Jacket  (ECG)   –  Zephyr  (heart  rate)  

Back  and  Frontoffice   •  Storage  and   visualisaKon   backoffices   •  Data  collecKon  and   storage  following   data  protecKon   guidelines   –  Informed  consent   –  Privacy   –  Data  ownership  

Mobility  Study   •  30  day  data  collecKon  campaign   •  10  users   •  ≈  40h  of  data   •  ≈  142k  GPS-­‐indexed  points  

Average  Speed  

Aggregate  Fuel  ConsumpKon  

Important  Places  

#  WiFi  APs  

Stress  Assessment   •  Example  study  case  with  single  driver   – hgp://   •  Example  longitudinal  study  with   quesKonnaires  and  recall  phase   – hgp://  

An  example  longitudinal  study  

CrowdSensor   Typical  Use  Cases   •  Urban  data  collecKon   •  Longitudinal  studies  on  specific  groups   – Police  officers,  bus  drivers,  firemen,  …   •  Algorithm  validaKon     – Machine  learning   – Context  inference   •  Service  acceptance,  user  trials    

What  you  can  do  with  it?   •  Configure  the  plaIorm  for  your  own   crowdsensing  applicaKon   – You  can  choose  only  a  subset  of  sensors  and  add   your  own  interface   – You  can  add  a  workflow   – You  can  add  geo-­‐located  quesKonnaires   – You  can  record  voice  answers     •  Join  as  parKcipant     •  Contact  us  for  designing  new  experiences  

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